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Record W4389259445 · doi:10.21203/rs.3.rs-3641432/v1

How does visual perception change for people with cognitive decline? A Scoping Review

2023· review· en· W4389259445 on OpenAlexafffund
Habib Chaudhury, Elizabeth A. Proctor

Bibliographic record

VenueResearch Square · 2023
Typereview
Languageen
FieldMedicine
TopicRetinal Imaging and Analysis
Canadian institutionsSimon Fraser University
FundersMitacs
KeywordsDementiaCognitive declineCognitionPsychologyVisual impairmentPerceptionAssociation (psychology)Cognitive impairmentClinical psychologyCognitive psychologyGerontologyDevelopmental psychologyMedicinePsychiatryDiseaseNeurosciencePsychotherapist

Abstract

fetched live from OpenAlex

Abstract Background Visual impairment and its associated functional limitations are a common experience of people living with cognitive decline; however, the underlying mechanisms are not fully understood. Identifying potentially modifiable risk factors for dementia and cognitive impairment is a vital step in developing effective sensory testing and intervention. Objective The current study is a scoping review of the literature investigating the association between visual changes and cognitive decline or dementia, and how this relates to functional difficulties. Design Online databases were searched to highlight relevant research from 2015-August 2022, of which we included 30 items in our final sample. Results The existing literature implicates visual impairment as a risk factor for cognitive decline, with 24 of the 30 studies reporting an association between visual impairment and cognitive decline. Conclusions Most of the studies found an association between visual impairment and cognitive decline, dementia, mild cognitive impairment or cognitive impairment-no dementia. Further research is needed to explore the mechanisms of action underpinning this relationship, including multiple measures of vision across various cognitive domains.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.465
Threshold uncertainty score0.858

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.275
GPT teacher head0.562
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes2
Has abstractyes

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